PhD students

1. PhD students are welcome to write to Dr. Shah directly for thesis research on; a) interpreting and developing novel generative and predictive deep neural networks for interrogating proximity relationships between biological pathways and image features to discover disease sub-types and real-world clinical deployment; b) formulating novel unsupervised, on, and off-policy AI learning methods for clinical data. **The standard process for all interested applicants is to first formally apply here: **https://apply.grad.uci.edu/apply/.
2. External students interested in computer science Ph.D. graduate studies can select Dr. Shah as the faculty PI and apply to the Electrical Engineering and Computer Science (https://engineering.uci.edu/dept/eecs/academics/graduate) program. Engineering students can apply to the Biomedical Engineering (https://engineering.uci.edu/dept/bme/graduate/prospective/admission-requirements) PhD program. Students interested in interdisciplinary studies in computation + wet lab research can apply to the Gateway PhD Graduate Program in Cellular & Molecular Biosciences (CMB).

MD-PhD students

Physician scientist (MD-PhD) students and applicants to the Medical Scientist Training Program interested in clinical informatics + molecular research are welcome to write to Dr. Shah directly to determine appropriate PhD component projects.

1. Undergraduate students interested in quarterly deep learning research methods or an UROP project are welcome to contact Dr. Shah directly and/or apply either to the https://undergraduate.bio.uci.edu/bio-197-198-199/ or the https://urop.uci.edu/urop-opportunities/ programs and indicate Prof. Shah as faculty advisor.
2. Interns and visiting scientists, please propose a topic and research plan that is relevant to the group.

Postdoctoral Scholar Position: Generative AI for Health and Medicine

The Computational Medicine Research Group led by Prof. Pratik Shah at the University of California, Irvine, invites applications for Postdoctoral Scholar position. The lab seeks outstanding Ph.D. or MD Ph.D. applicants with strong academic backgrounds in computer science, biomedical informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world clinical decision-making from non-imaging datasets, with research published in top journals such as Cell Reports Methods, Nature Digital Medicine, JAMA, IEEE Conferences, and Proceedings of National Academies of Science Engineering and Medicine Workshops. Opportunities and training to publish in leading biomedical journals and machine learning conferences, networking with government funding agencies, industry partners, foundations, and academic experts. Training in fellowship writing, teaching/mentoring, oral presentations and review of manuscripts will be provided. For more information about the research group, publications, projects, and Prof. Shah, please visit: https://faculty.sites.uci.edu/pratikshahlab/; https://www.pratiks.info/

The candidate will contribute to two key research areas:

1. Generative AI for Medical Imaging and Digital Biopsies: Develop and interpret deep neural networks (DNNs) for automating non-destructive tissue-based analyses using high-parameter medical images (e.g., pathology, MRI, CT, and RGB) and molecular profiles. Conduct hypothesis-driven research to link molecular profiles to disease biology. Focus on generating and validating diagnostic tools for clinical use.
2. Generative and Predictive AI for Clinical Decision Support and Statistical Inference: Develop biologically informed statistical methods and uncertainty estimation models to train deep learning models for clinical decision-making from EMRs and genetic sequencing data. Focus on predicting patient outcomes and discover novel biologically relevant disease subtypes.

Responsibilities:

• Collecting, preprocessing, and visualizing high-dimensional medical images, non-imaging clinical (EMR), and genetic sequencing datasets • Training and validating generative deep learning (e.g., GANs, Diffusion, Transformers) and deep reinforcement learning models • Developing novel statistical models for uncertainty quantification, causality estimation, and prediction accuracy • Publishing research in leading biomedical and machine learning journals and conferences • Engaging with industry partners, government agencies, and academic experts • Strong analytical, organizational, and communication skills • Committed to mentoring and teaching

Required Qualifications:

PhD. or MD, PhD. in computer science, biomedical informatics, engineering, statistics, or a related field either at the time of application or be working towards the PhD.

• Preferred Qualifications: • Familiarity with data processing techniques for medical imaging, time-series clinical EMR, and genetic sequencing data • Previous experience leveraging deep learning libraries (e.g., OpenCV, Theano, Caffe, Keras, TensorFlow) and machine learning models/datasets (e.g., AlexNet, ImageNet, MNIST, MySQL, MongoDB) • Experience with probabilistic models, Bayesian methods, reinforcement learning, and uncertainty quantification from imaging and non-imaging clinical EMR and genetic sequencing data • Expertise in programming (Python, MATLAB, C++, Java) and software development in a collaborative environment using version control (Git, GitHub), issue tracking, and code review • Track record of writing and publishing first-author research papers in peer-reviewed journals or top machine learning conferences

TO APPLY: Please log onto UC Irvine’s RECRUIT located at: https://recruit.ap.uci.edu/JPF09528

Document requirements

• Curriculum Vitae - Your most recently updated C.V.
• Cover Letter - A cover letter with career motivations and why you are interested in joining the lab
• Statement of Research
• Teaching Statement - See guidance for writing a reflective teaching statement.
• Inclusive Excellence Activities Statement - Statement addressing how past and/or potential contributions to inclusive excellence will advance UCI’s Commitment to Inclusive Excellence. See guidance for writing an inclusive excellence activities statement.
• Reference requirements: 3 required (contact information only)

Emergency Medicine and Critical Care Collaborators:

Attending clinicians (MDs), residents and fellows are welcome to collaborate with us as we create new clinical models driven by artificial intelligence (AI) to analyze data from hundreds of thousands of de-identified EMR records from emergency and intensive care departments nationwide. Clinical collaborators will work with computer scientists to evaluate the data for early diagnosis of diseases, therapeutic choices, and evaluating confounders in treatment effects of medications. You will be able to work remotely to review the data via a secure online web portal, but are welcome spend time in lab. Clinicians may also collaborate as authors on research publications from this project and learn machine-learning and informatics skills. We welcome clinicians interested in developing cutting-edge health related AI technologies by using clinical principles.

Clinical and Anatomic Pathology Collaborators:

Attending clinicans (MDs), residents and fellows are welcome to collaborate with us as we create new clinical models driven by artificial intelligence (AI) to analyze data from hundreds of thousands of de-identified histopathology images and molecular panels from nationwide hospitals. Medical collaborators will work with computer scientists to clinically evaluate the images for different purposes such as early diagnosis of diseases, therapeutic choices, tumors, genomic profiles, and patient stratification. You will be able to work remotely to review the images via a secure online web portal, but are welcome spend time in lab. Clinicians may also collaborate as authors on research publications from this project and learn machine-learning and informatics skills. We welcome clinicians interested in developing cutting-edge health related AI technologies by using clinical principles.